Una combinación basada en operadores OWA para la Clasificación de Género Multi-etiqueta de páginas web

This paper presents a new method for genre identification that combines homogeneous classifiers using OWA (Ordered Weighted Averaging) operators. Our method uses character n-grams extracted from different information sources such as URL, title, headings and anchors. To deal with the complexity of web pages, we applied MLKNN as a multi-label classifier, in which a web page can be affected by more than one genre. Experiments conducted using a known multi-label corpus show that our method achieves good results.En este trabajo se presenta un nuevo método para la identificación de género que combina clasificadores homogéneos utilizando OWA (promedio ponderado) Pedimos operadores. Nuestro método utiliza caracteres n-gramas extraídos de diferentes fuentes de información, tales como URL, título, encabezados y anclajes. Para hacer frente a la complejidad de las páginas web, se aplicó MLKNN como un clasificador multi-etiqueta, en el que una página web puede verse afectada por más de un género. Los experimentos llevados a cabo usando un conocido corpus multi-etiqueta muestran que nuestro método logra buenos resultados

Human Annotation and Automatic Detection of Web Genres

Texts differ from each other in various dimensions such as topic, sentiment, authorship and genre. In this thesis, the dimension of text variation of interest is genre. Unlike topic classification, genre classification focuses on the functional purpose of documents and classifies them into categories such as news, review, online shop, personal home page and conversational forum. In other words, genre classification allows the identification of documents that are similar in terms of purpose, even they are topically very diverse. Research on web genres has been motivated by the idea that finding information on the web can be made easier and more effective by automatic classification techniques that differentiate among web documents with respect to their genres. Following this idea, during the past two decades, researchers have investigated the performance of various genre classification algorithms in order to enhance search engines. Therefore, current web automatic genre identification research has resulted in several genre annotated web-corpora as well as a variety of supervised machine learning algorithms on these corpora. However, previous research suffers from shortcomings in corpus collection and annotation (in particular, low human reliability in genre annotation), which then makes the supervised machine learning results hard to assess and compare to each other as no reliable benchmarks exist. This thesis addresses this shortcoming. First, we built the Leeds Web Genre Corpus Balanced-design (LWGC-B) which is the first reliably annotated corpus for web genres, using crowd-sourcing for genre annotation. This corpus which was compiled by focused search method, overcomes the drawbacks of previous genre annotation efforts such as low inter-coder agreement and false correlation between genre and topic classes. Second, we use this corpus as a benchmark to determine the best features for closed-set supervised machine learning of web genres. Third, we enhance the prevailing supervised machine learning paradigm by using semi-supervised graph-based approaches that make use of the graph-structure of the web to improve classification results. Forth, we expanded our annotation method successfully to Leeds Web Genre Corpus Random (LWGC-R) where the pages to be annotated are collected randomly by querying search engines. This randomly collected corpus also allowed us to investigate coverage of the underlying genre inventory. The result shows that our 15 genre categories are sufficient to cover the majority but not the vast majority of the random web pages. The unique property of the LWGC-R corpus (i.e. having web pages that do not belong to any of the predefined genre classes which we refer to as noise) allowed us to, for the first time, evaluate the performance of an open-set genre classification algorithm on a dataset with noise. The outcome of this experiment indicates that automatic open-set genre classification is a much more challenging task compared to closed-set genre classification due to noise. The results also show that automatic detection of some genre classes is more robust to noise compared to other genre classes

Deep Learning Methods for Register Classification

For this project the data used is the one collected by, Biber and Egbert (2018) related to various language articles from the internet. I am using BERT model (Bidirectional Encoder Representations from Transformers), which is a deep neural network and FastText, which is a shallow neural network, as a baseline to perform text classification. Also, I am using Deep Learning models like XLNet to see if classification accuracy is improved. Also, it has been described by Biber and Egbert (2018) what is register. We can think of register as genre. According to Biber (1988), register is varieties defined in terms of general situational parameters. Hence, it can be inferred that there is a close relation between the language and the context of the situation in which it is being used. This work attempts register classification using deep learning methods that use attention mechanism. Working with the models, dealing with the imbalanced datasets in real life problems, tuning the hyperparameters for training the models was accomplished throughout the work. Also, proper evaluation metrics for various kind of data was determined. The background study shows that how cumbersome the use classical Machine Learning approach used to be. Deep Learning, on the other hand, can accomplish the task with ease. The metric to be selected for the classification task for different types of datasets (balanced vs imbalanced), dealing with overfitting was also accomplished

Latin Etymologies as Features on BNC Text Categorization

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Two-Step Cluster Based Feature Discretization of Naive Bayes for Outlier Detection in Intrinsic Plagiarism Detection

Intrinsic plagiarism detection is the task of analyzing a document with respect to undeclared changes in writing style which treated as outliers. Naive Bayes is often used to outlier detection. However, Naive Bayes has assumption that the values of continuous feature are normally distributed where this condition is strongly violated that caused low classification performance. Discretization of continuous feature can improve the performance of Naïve Bayes. In this study, feature discretization based on Two-Step Cluster for Naïve Bayes has been proposed. The proposed method using tf-idf and query language model as feature creator and False Positive/False Negative (FP/FN) threshold which aims to improve the accuracy and evaluated using PAN PC 2009 dataset. The result indicated that the proposed method with discrete feature outperform the result from continuous feature for all evaluation, such as recall, precision, f-measure and accuracy. The using of FP/FN threshold affects the result as well since it can decrease FP and FN; thus, increase all evaluation

BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology

This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software

Modeling Non-Standard Text Classification Tasks

Text classification deals with discovering knowledge in texts and is used for extracting, filtering, or retrieving information in streams and collections. The discovery of knowledge is operationalized by modeling text classification tasks, which is mainly a human-driven engineering process. The outcome of this process, a text classification model, is used to inductively learn a text classification solution from a priori classified examples. The building blocks of modeling text classification tasks cover four aspects: (1) the way examples are represented, (2) the way examples are selected, (3) the way classifiers learn from examples, and (4) the way models are selected. This thesis proposes methods that improve the prediction quality of text classification solutions for unseen examples, especially for non-standard tasks where standard models do not fit. The original contributions are related to the aforementioned building blocks: (1) Several topic-orthogonal text representations are studied in the context of non-standard tasks and a new representation, namely co-stems, is introduced. (2) A new active learning strategy that goes beyond standard sampling is examined. (3) A new one-class ensemble for improving the effectiveness of one-class classification is proposed. (4) A new model selection framework to cope with subclass distribution shifts that occur in dynamic environments is introduced